Self-position estimation device, self-position estimation method, program, and recording medium

ABSTRACT

A self-position estimation device is mounted on a mobile body and acquires a predicted position of the mobile body. Additionally, the self-position estimation device calculates a difference value between a predicted position of an end point of a lane division line that is obtained based on information on the end point of the lane division line acquired from map information and a measured position of the end point of the lane division line measured in such a manner that a measurement unit mounted on the mobile body performs scanning with a light in a predetermined direction. The self-position estimation device estimates a self-position of the mobile body by correcting the predicted position with a value obtained by multiplying the difference value by a coefficient. Further, the self-position estimation device corrects the coefficient based on an interval of scanning positions of the measurement unit at a position where the end point of the lane division line is detected.

TECHNICAL FIELD

The present invention relates to a self-position estimation technology.

BACKGROUND TECHNIQUE

A technology that detects ground objects installed on a traveling routeof a vehicle by using a radar and a camera and corrects an own-vehicleposition based on a result of the detection has been known. For example,Patent Reference 1 discloses a technology that collates an output of ameasurement sensor and positional information of a ground objectpreviously registered in a map, to estimate a self-position. Further,Patent Reference 2 discloses an own-vehicle position estimationtechnology using a Kalman filter.

PRIOR ART REFERENCES Patent References

Patent Reference 1: Japanese Patent Laid-Open No. 2013-257742

Patent Reference 2: Japanese Patent Laid-Open No. 2017-72422

SUMMARY OF THE INVENTION Problem to be Solved by the Invention

In a case where a continuously-provided structure such as a white line,a curbstone, and a guardrail is measured in the self-position estimationbased on a Bayesian method disclosed in Patent Reference 2, a distancein a lateral direction viewed from the own vehicle can be measured, buta distance in a traveling direction cannot be accurately specified dueto continuity in the traveling direction. In addition, in a case of alane division line in a dashed line shape, a length of the lane divisionline to be detected in a front-rear direction is changed. Therefore, theposition in the traveling direction cannot be accurately specifiedsimilarly.

The present invention is made to solve the above-described issues, andan object of the present invention is to provide a self-positionestimation device that handles a lane division line such as a white lineas a measurement target, and makes it possible to perform positionestimation not only in a lateral direction but also in a travelingdirection with high accuracy.

Means for Solving the Problem

An invention according to claims is a self-position estimation devicemounted on a mobile body comprising: an acquisition unit configured toacquire a predicted position of the mobile body; a difference valuecalculation unit configured to calculate a difference value between apredicted position of an end point of a lane division line and ameasured position of the end point of the lane division line, thepredicted position being obtained based on information on the end pointof the lane division line acquired from map information, the measuredposition being measured by a measurement unit which is mounted on themobile body and which measures the measured position by performingscanning with a light in a predetermined direction; and an estimationunit configured to estimate a self-position of the mobile body bycorrecting the predicted position of the mobile body with a valueobtained by multiplying the difference value by a coefficient, whereinthe estimation unit corrects the coefficient based on an interval ofscanning positions of the measurement unit at a position where the endpoint of the lane division line is detected.

Another invention according to claims is a self-position estimationmethod performed by a self-position estimation device mounted on amobile body, the method comprising: an acquisition process configured toacquire a predicted position of the mobile body; a difference valuecalculation process configured to calculate a difference value between apredicted position of an end point of a lane division line and ameasured position of the end point of the lane division line, thepredicted position being obtained based on information on the end pointof the lane division line acquired from map information, the measuredposition being measured by a measurement unit which is mounted on themobile body and which measures the measured position by performingscanning with a light in a predetermined direction; and an estimationprocess configured to estimate a self-position of the mobile body bycorrecting the predicted position of the mobile body with a valueobtained by multiplying the difference value by a coefficient, whereinthe estimation process corrects the coefficient based on an interval ofscanning positions of the measurement unit at a position where the endpoint of the lane division line is detected.

Still another invention according to claims is a program executed by aself-position estimation device that is mounted on a mobile body andincludes a computer, the program causing the computer to function as: anacquisition unit configured to acquire a predicted position of themobile body; a difference value calculation unit configured to calculatea difference value between a predicted position of an end point of alane division line and a measured position of the end point of the lanedivision line, the predicted position being obtained based oninformation on the end point of the lane division line acquired from mapinformation, the measured position being measured by a measurement unitwhich is mounted on the mobile body and which measures the measuredposition by performing scanning with a light in a predetermineddirection; and an estimation unit configured to estimate a self-positionof the mobile body by correcting the predicted position of the mobilebody with a value obtained by multiplying the difference value by acoefficient, wherein the estimation unit corrects the coefficient basedon an interval of scanning positions of the measurement unit at aposition where the end point of the lane division line is detected.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic configuration diagram of a driving support system.

FIG. 2 is a block diagram illustrating a functional configuration of anonboard device.

FIG. 3 is a diagram illustrating a state variable vector in atwo-dimensional orthogonal coordinate system.

FIG. 4 is a diagram illustrating schematic relationship between aprediction step and a measurement update step.

FIG. 5 is a diagram illustrating functional blocks of an own-vehicleposition estimation unit.

FIG. 6 is a diagram illustrating a method of converting a lidarmeasurement value into a value in a Cartesian coordinate system.

FIG. 7 is a diagram illustrating a method of detecting a white line by alidar mounted on a vehicle.

FIG. 8 is a diagram illustrating positions of a plurality of scan linesin a window.

FIG. 9 is a diagram illustrating a method of detecting a position of anend point at a starting part of a white line.

FIG. 10 is a diagram illustrating a method of detecting a position of anend point at a termination part of the white line.

FIG. 11 is a diagram illustrating a method of determining the positionof the end point in a case where reflection intensity is changed inmiddle of a scan line.

FIG. 12 is a diagram illustrating a method of calculating a landmarkprediction value of the end point of the white line.

FIGS. 13A and 13B are diagrams illustrating correction coefficients of aKalman gain.

FIGS. 14A and 14B are diagrams illustrating correction coefficients ofmeasurement noise.

FIG. 15 is a flowchart of an own-vehicle position estimation processing.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

According to one aspect of the present invention, there is provided aself-position estimation device mounted on a mobile body, comprising: anacquisition unit configured to acquire a predicted position of themobile body; a difference value calculation unit configured to calculatea difference value between a predicted position of an end point of alane division line and a measured position of the end point of the lanedivision line, the predicted position being obtained based oninformation on the end point of the lane division line acquired from mapinformation, the measured position being measured by a measurement unitwhich is mounted on the mobile body and which measures the measuredposition by performing scanning with a light in a predetermineddirection; and an estimation unit configured to estimate a self-positionof the mobile body by correcting the predicted position of the mobilebody with a value obtained by multiplying the difference value by acoefficient, wherein the estimation unit corrects the coefficient basedon an interval of scanning positions of the measurement unit at aposition where the end point of the lane division line is detected.

The above-described self-position estimation device is mounted on themobile body and acquires the predicted position of the mobile body. Theself-position estimation device calculates the difference value betweenthe predicted position of the end point of the lane division line thatis obtained based on the information on the end point of the lanedivision line acquired from the map information and the measuredposition of the end point of the lane division line measured in such amanner that the measurement unit mounted on the mobile body performsscanning with a light in a predetermined direction, and estimates theself-position of the mobile body by correcting the predicted position ofthe mobile body with the value obtained by multiplying the differencevalue by the coefficient. Then, the self-position estimation devicecorrects the coefficient based on the interval of the scanning positionsof the measurement unit at the position where the end point of the lanedivision line is detected. According to the self-position estimationdevice, it is possible to enhance estimation accuracy of theself-position in the traveling direction of the mobile body by using theposition of the end point of the lane division line. In a preferredexample, the coefficient is a Kalman gain.

In one mode of the above-described self-position estimation device, theestimation unit corrects the coefficient with an inverse number of theinterval of the scanning positions. In another mode, the estimation unitcorrects the coefficient based on a ratio of the interval of thescanning positions and a shortest interval of the scanning positionsmeasured by the measurement unit among intervals of the scanningpositions. In this mode, the coefficient is corrected such that thedifference value is largely reflected in the self-position estimation asthe interval of the scanning positions is smaller.

In still another mode of the above-described self-position estimationdevice, the measurement unit detects a lane division line existing in awindow that is defined at a predetermined position with a position ofthe mobile body as a reference, and the estimation unit corrects thecoefficient based on a ratio of number of scan lines existing on thelane division line and number of scan lines previously determined basedon a size of the window. In this mode, the coefficient is corrected suchthat the difference value is largely reflected in the self-positionestimation as the number of scan lines existing on the detected lanedivision line is larger.

In still another mode of the above-described self-position estimationdevice, the measurement unit detects a middle point between a scan lineexisting on the lane division line and a scan line not existing on thelane division line as the end point of the lane division line. The scanline existing on the lane division line and the scan line not existingon the lane division line are adjacent to each other. This makes itpossible to determine the end point of the lane division line from aposition of the scan line existing on the lane division line and aposition of the scan line not existing on the lane division line.

According to another aspect of the present invention, there is provideda self-position estimation method performed by a self-positionestimation device mounted on a mobile body, the method comprising: anacquisition process configured to acquire a predicted position of themobile body; a difference value calculation process configured tocalculate a difference value between a predicted position of an endpoint of a lane division line and a measured position of the end pointof the lane division line, the predicted position being obtained basedon information on the end point of the lane division line acquired frommap information, the measured position being measured by a measurementunit which is mounted on the mobile body and which measures the measuredposition by performing scanning with a light in a predetermineddirection; and an estimation process configured to estimate aself-position of the mobile body by correcting the predicted position ofthe mobile body with a value obtained by multiplying the differencevalue by a coefficient, wherein the estimation process corrects thecoefficient based on an interval of scanning positions of themeasurement unit at a position where the end point of the lane divisionline is detected. According to the self-position estimation method, itis possible to enhance estimation accuracy of the self-position in thetraveling direction of the mobile body by using the position of the endpoint of the lane division line.

According to still another aspect of the present invention, there isprovided a program executed by a self-position estimation device that ismounted on a mobile body and includes a computer, the program causingthe computer to function as: an acquisition unit configured to acquire apredicted position of the mobile body; a difference value calculationunit configured to calculate a difference value between a predictedposition of an end point of a lane division line and a measured positionof the end point of the lane division line, the predicted position beingobtained based on information on the end point of the lane division lineacquired from map information, the measured position being measured by ameasurement unit which is mounted on the mobile body and which measuresthe measured position by performing scanning with a light in apredetermined direction; and an estimation unit configured to estimate aself-position of the mobile body by correcting the predicted position ofthe mobile body with a value obtained by multiplying the differencevalue by a coefficient, wherein the estimation unit corrects thecoefficient based on an interval of scanning positions of themeasurement unit at a position where the end point of the lane divisionline is detected. By executing the program by the computer, theabove-described self-position estimation device can be realized. Theprogram can be handled by being stored in a recording medium.

Embodiments

A preferred embodiment of the present invention is described below withreference to drawings. Note that a character in which a symbol“{circumflex over ( )}” or “−” is added above an optional character isexpressed by “A{circumflex over ( )}” or “A ” (“A” is optionalcharacter) for convenience in the present specification.

[Driving Support System]

FIG. 1 is a schematic configuration diagram of a driving support systemaccording to the present embodiment. The driving support systemillustrated in FIG. 1 includes an onboard device 1 that is mounted on avehicle and performs control relating to driving support of the vehicle,a lidar (light detection and ranging, or laser illuminated detection andranging) 2, a gyro sensor 3, a vehicle speed sensor 4, and a GPSreceiver 5.

The onboard device 1 is electrically connected to the lidars 2, the gyrosensor 3, the vehicle speed sensor 4, and the GPS receiver 5, andestimates a position of a vehicle (also referred to as “own-vehicleposition”) on which the onboard device 1 is mounted, based on outputsfrom the lidars 2, the gyro sensor 3, the vehicle speed sensor 4, andthe GPS receiver 5. Further, the onboard device 1 performs automaticdriving control and the like of the vehicle such that the vehicletravels along a route to a set destination based on a result of theestimation of the own-vehicle position. The onboard device 1 stores amap database (DB) 10 in which road data and information about landmarkground objects and lane division lines provided near a road areregistered. Examples of the above-described landmark ground objectsinclude kilometer posts, 100-meter posts, delineators, trafficinfrastructure facilities (e.g., traffic signs, destination boards, andsignals), telephone poles, and streetlights, periodically arranged alongthe road. The onboard device 1 collates the outputs from the lidars 2with the map DB 10 to estimate the own-vehicle position. The onboarddevice 1 is an example of a “self-position estimation device” in thepresent invention.

Each of the lidars 2 emit a pulse laser to a predetermined angle rangein a horizontal direction and a vertical direction to discretely measurea distance to an object existing outside, and generatesthree-dimensional point group information representing a position of theobject. In this case, each of the lidar 2 includes an irradiation unit,a light reception unit, and an output unit. The irradiation unitperforms irradiation with a laser beam while changing an irradiationdirection. The light reception unit receives reflected light (scatteredlight) of the irradiated laser beam. The output unit outputs scan databased on a light reception signal output from the light reception unit.In the present embodiment, an irradiation range of the laser emitted bythe lidar 2 includes at least a road surface of the road. The scan datais generated based on the irradiation direction corresponding to thelaser beam received by the light reception unit and a distance to theobject in the irradiation direction of the laser beam. The distance tothe object is specified based on the above-described light receptionsignal. Generally, accuracy of the distance measurement value by thelidar is higher as the distance to the object is smaller, and theaccuracy is lower as the distance is larger. The lidars 2, the gyrosensor 3, the vehicle speed sensor 4, and the GPS receiver 5 each supplyoutput data to the onboard device 1. The lidar 2 is example of a“measurement unit” in the present invention.

FIG. 2 is a block diagram illustrating a functional configuration of theonboard device 1. The onboard device 1 mainly includes an interface 11,a storage unit 12, an input unit 14, a control unit 15, and aninformation output unit 16. These elements are connected to one anotherthrough a bus line.

The interface 11 acquires the output data from the sensors such as thelidar 2, the gyro sensor 3, the vehicle speed sensor 4, and the GPSreceiver 5, and supplies the output data to the control unit 15.

The storage unit 12 stores programs to be executed by the control unit15, and information necessary for the control unit 15 to performpredetermined processing. In the present embodiment, the storage unit 12stores the map DB 10 including lane division line information and groundobject information. The lane division line information is informationabout a lane division line (white line) provided in each road, andincludes coordinate information representing a discrete position of eachlane division line. Note that the lane division line information may beinformation incorporated in the road data of each road. Further, in thepresent embodiment, as for a lane division line of a dashed line, thelane division line information includes coordinate informationrepresenting positions of end points of the lane division line. Theground object information is information about a ground object otherthan the lane division line. In this example, at least ground object IDcorresponding to an index of a ground object and positional informationthat indicates an absolute position of the ground object represented bylatitude, longitude, (and altitude), etc. are associated with each otherfor each ground object. Note that the map DB 10 may be periodicallyupdated. In this case, for example, the control unit 15 receives partialmap information about an area to which the own-vehicle position belongs,from a server apparatus managing the map information through anunillustrated communication unit, and reflects the partial mapinformation in the map DB 10.

The input unit 14 includes a button, a touch panel, a remote controller,an audio input device, and the like operated by a user. The informationoutput unit 16 includes, for example, a display and a speaker thatperform output based on control by the control unit 15.

The control unit 15 includes a CPU executing a program, etc., andcontrols the whole of the onboard device 1. In the present embodiment,the control unit 15 includes an own-vehicle position estimation unit 17.The control unit 15 is an example of an acquisition unit, a differencevalue calculation unit, and an estimation unit in the present invention.

The own-vehicle position estimation unit 17 corrects the own-vehicleposition estimated from the output data of the gyro sensor 3, thevehicle speed sensor 4, and/or the GPS receiver 5, based on themeasurement values of the distance and the angle of a landmark by thelidar 2 and the positional information on the landmark extracted fromthe map DB 10. In the present embodiment, as an example, the own-vehicleposition estimation unit 17 alternately performs a prediction step ofpredicting the own-vehicle position from the output data of the gyrosensor 3, the vehicle speed sensor 4, and the like based on a stateestimation method based on Bayesian estimation and a measurement updatestep of correcting a predicted value of the own-vehicle positioncalculated in the immediately preceding prediction step. As a stateestimation filter used in these steps, various filters developed forBayesian estimation are usable, and examples of the state estimationfilter include an extended Kalman filter, an unscented Kalman filter,and a particle filter. As described above, various methods are proposedas the position estimation method based on Bayesian estimation.

In the following, the own-vehicle position estimation using the extendedKalman filter is briefly described. As described below, in the presentembodiment, even in a case of the own-vehicle position estimation basedon any of the ground objects and the lane division lines, theown-vehicle position estimation unit 17 consistently performs theown-vehicle position estimation processing using the extended Kalmanfilter.

FIG. 3 is a diagram illustrating a state variable vector x in atwo-dimensional orthogonal coordinate system. As illustrated in FIG. 3,the own-vehicle position on a plane defined in the two-dimensionalorthogonal coordinate of x and y is represented by coordinates “(x, y)”and an azimuth of own vehicle “Ψ”. The azimuth Ψ is defined as an angleformed by the traveling direction of the vehicle and the x-axis. Thecoordinates (x, y) represent an absolute position in a coordinate systemthat has a certain reference position as an origin, corresponding to,for example, a combination of latitude and longitude.

FIG. 4 is a diagram illustrating schematic relationship between theprediction step and the measurement update step. In addition, FIG. 5illustrates exemplary functional blocks of the own-vehicle positionestimation unit 17. As illustrated in FIG. 4, the prediction step andthe measurement update step are repeated to successively performcalculation and update of the estimated value of a state variable vector“X” representing the own-vehicle position. Further, as illustrated inFIG. 5, the own-vehicle position estimation unit 17 includes a positionprediction unit 21 performing the prediction step, and a positionestimation unit 22 performing the measurement update step. The positionprediction unit 21 includes a dead reckoning block 23 and a positionprediction block 24. The position estimation unit 22 includes a landmarksearch/extraction unit 25 and a position correction block 26. Note that,in FIG. 4, the state variable vector at a reference time (i.e., currenttime) “t” to be calculated is expressed by “X (t)” or “X{circumflex over( )}(t)”. The state variable vector is expressed as “state variablevector X(t)=(x(t), y(t), ψ(t))^(T)”. In this example, a tentativeestimated value (predicted value) estimated in the prediction step isadded with a symbol “ ” above a character representing the predictedvalue, and an estimated value with higher accuracy updated in themeasurement update step is added with a symbol “{circumflex over ( )}”above the character representing the value.

In the prediction step, the dead reckoning block 23 of the own-vehicleposition estimation unit 17 determines a moving distance and azimuthchange from a preceding time by using a moving speed “v” and an angularvelocity “ω” of the vehicle (these are collectively expressed by“control value u(t)=(v(t), ω(t))^(T)”). The position prediction block 24of the own-vehicle position estimation unit 17 adds the determinedmoving distance and the determined azimuth change to the state variablevector X{circumflex over ( )}(t−1) at the time t−1 calculated in theimmediately preceding measurement update step, thereby calculating thepredicted value of the own-vehicle position (also referred to as“predicted own-vehicle position”) X (t) at the time t. In addition, atthe same time, the position prediction block 24 of the own-vehicleposition estimation unit 17 calculates a covariance matrix “P (t)”corresponding to error distribution of the predicted own-vehicleposition X (t), from a covariance matrix “P{circumflex over ( )}(t−1)”at the time t−1 calculated in the immediately preceding measurementupdate step.

In the measurement update step, the landmark search/extraction unit 25of the own-vehicle position estimation unit 17 associates the positionvector of the landmark registered in the map DB 10 with the scan data ofthe lidars 2. Further, in a case where the association is performable,the landmark search/extraction unit 25 of the own-vehicle positionestimation unit 17 acquires a measurement value of the associatedlandmark (referred to as “landmark measurement value”) “Z(t)” by thelidars 2 and an estimated measurement value of the landmark (referred toas “landmark prediction value”) “Z (t)”. The landmark prediction valueZ (t) is obtained by modeling the measurement processing by the lidars 2using the predicted own-vehicle position X (t) and the position vectorof the landmark registered in the map DB 10. The landmark measurementvalue Z(t) is a two-dimensional vector in a vehicle body coordinatesystem that is obtained by converting the distance and the scan angle ofthe landmark measured by the lidars 2 at the time t into components inthe traveling direction and the lateral direction of the vehicle. Theposition correction block 26 of the own-vehicle position estimation unit17 calculates a difference value between the landmark measurement valueZ(t) and the landmark prediction value Z (t) as expressed by thefollowing expression (1).

$\begin{matrix}{{{Z(t)} - {\overset{¯}{Z}(t)}} = \begin{bmatrix}{dx} \\{dy}\end{bmatrix}} & (1)\end{matrix}$

As expressed by the following expression (2), the position correctionblock 26 of the own-vehicle position estimation unit 17 multiplies thedifference value between the landmark measurement value Z(t) and thelandmark prediction value Z (t) by a Kalman gain “K(t)”, and adds theresult of the multiplication to the predicted own-vehicle positionX (t), thereby calculating an updated state variable vector (alsoreferred to as “estimated own-vehicle position”) X{circumflex over( )}(t).

{circumflex over (X)}(t)= X (t)+K(t){Z(t)− Z (t)}  (2)

Further, in the measurement update step, the position correction block26 of the own-vehicle position estimation unit 17 determines acovariance matrix P{circumflex over ( )}(t) (also simply referred to asP(t)) corresponding to the error distribution of the estimatedown-vehicle position X{circumflex over ( )}(t), from the covariancematrix P (t), in a manner similar to the prediction step. The parameterssuch as the Kalman gain K(t) can be calculated in a manner similar to,for example, a well-known self-position estimation technology using theextended Kalman filter.

As described above, the prediction step and the measurement update stepare repeatedly performed, and the predicted own-vehicle position X (t)and the estimated own-vehicle position X{circumflex over ( )}(t) aresuccessively calculated. As a result, the most probable own-vehicleposition is calculated.

[Own-Vehicle Position Estimation Using Lane Division Line]

Next, the own-vehicle position estimation using the lane division lines,that is characteristic in the present embodiment, is described. In thepresent embodiment, the own-vehicle position estimation is performedusing the lane division lines as landmarks. Note that, in the followingdescription, an example in which white lines are used as the lanedivision lines is described. However, the own-vehicle positionestimation is similarly applicable to yellow lane division lines.

(1) Measurement of White Line by Lidar

First, a method of measuring the white line by the lidar is described.

(Measurement Value by Lidar)

As illustrated in FIG. 6, a Cartesian coordinate system (hereinafter,referred to as “vehicle coordinate system”) in which an own-vehicleposition of a vehicle is handled as an origin and the travelingdirection of the vehicle is represented in the x-axis is considered. Themeasurement value to a measurement target by the lidar includes ahorizontal angle α, a vertical angle β, and a distance r. When thesevalues are converted into values in the Cartesian coordinate system, themeasurement value by the lidar is expressed by the following expression.

$\begin{matrix}{{Z(i)} = {\begin{bmatrix}{r_{x}(i)} \\{r_{y}(i)} \\{r_{z}(i)}\end{bmatrix} = \begin{bmatrix}{{r(i)}\cos \mspace{11mu} \beta \; (i)\cos \mspace{11mu} {\alpha (i)}} \\{{r(i)}\cos \mspace{11mu} {\beta (i)}\sin \mspace{11mu} {\alpha (i)}} \\{{r(i)}\sin \mspace{11mu} {\beta (i)}}\end{bmatrix}}} & (3)\end{matrix}$

(Detection of White Line)

FIG. 7 illustrates a method of detecting a white line by each of thelidars 2 mounted on the vehicle. The white line has high reflectionintensity because a retroreflective material is applied. Accordingly,the onboard device 1 provides a window at a position where the whiteline on a road surface in front of the vehicle is easily detectable, anddetects a portion having high reflection intensity in the window as thewhite line.

Specifically, as illustrated in FIG. 7, emission light EL is emittedtoward the road surface in front of the vehicle from each of the lidars2 provided on a front right side and a front left side of the vehicle.Virtual windows W are defined at predetermined positions in front of thevehicle. The positions of the windows W based on a center O of thevehicle are previously determined. Namely, a horizontal distance L1 fromthe center O of the vehicle to the windows W, a lateral-directiondistance L2 from the center O of the vehicle to each of the right windowW and the left window W, and a length L3 of each of the windows W ispreviously determined. The onboard device 1 extracts the scan databelonging to each of the windows W among the scan data output from thelidars 2. In a case where the reflection intensity of the scan data islarger than a predetermined threshold, the onboard device 1 determinesthat the white line has been detected.

(Line Interval)

When multilayer lidars are used as the lidars 2, a plurality of scanlines s are formed in each of the windows W. FIG. 8 illustratespositions of the plurality of scan lines s in each of the windows W.Intervals of the plurality of scan lines s (hereinafter, referred to as“line intervals”) in each of the windows W are gradually increased as adistance from the vehicle is increased. An upper right part in FIG. 8 isa plane view of the scan lines in one window W as viewed from above.Scan lines s₁ to s₁₀ are formed in the window W, and line intervals d₁to d₉ of the scan lines are illustrated. The line interval d of the scanline is increased as the distance from the vehicle is increased. A lowerright part in FIG. 8 is a diagram of the scan lines in one window W asviewed from a side. Likewise, the line interval d of the scan line isincreased as the distance from the vehicle is increased.

When a number of an arbitrary scan line is defined as “i”, a horizontalangle of an i-th scan line is defined as α(i), a vertical angle isdefined as β(i), and a distance from the vehicle is defined as r(i), aline interval d(i) between the i-th scan line and an (i+1)-th scan lineis determined by the following expression (4). Likewise, a line intervald(i+1) between the (i+1)-th scan line and an (i+2)-th scan line isdetermined by the following expression (5), and a line interval d(i+2)between the (i+2)-th scan line and an (i+3)-th scan line is determinedby the following expression (6).

d(i)=r(i)cos α(i)cos β(i)−r(i+1)cos α(i+1)cos β(i+1)   (4)

d(i+1)=r(i+1)cos α(i+1)cos β(i+1)−r(i+2)cos α(i+2)cos β(i+2)   (5)

d(i+2)=r(i+2)cos α(i+2)cos β(i+2)−r(i+3)cos α(i+3)cos β(i+2)   (6)

As described above, the line intervals d of the plurality of scan liness in each of the windows W can be determined based on the scan data bythe lidars 2.

(Measurement of End Point Position of White Line)

Next, a method of measuring an end point position of the white line bythe lidar is described. FIG. 9 illustrates a method of measuring theposition of the end point of a starting part of the white line (startingpoint of white line). In the window W, 10 scan lines s₁ to s₁₀ areformed. As described above, the onboard device 1 detects the white linebased on the reflection intensity of the scan data obtained by thelidar. It is now assumed that the reflection intensity of the scan datais changed between the scan lines s₂ and s₃ as illustrated in theexample 1. Namely, the reflection intensity of the scan line s₂ islarger than or equal to a predetermined threshold, and the reflectionintensity of the scan line s₃ is smaller than the predeterminedthreshold. In this case, it is known that the end point of the whiteline exists between the scan lines s₂ and s₃, but the accurate positionof the end point of the white line is not known. Accordingly, theonboard device 1 regards a middle point between the scan lines s₂ and s₃as the end point of the white line.

Likewise, in a case where the reflection intensity of the scan data ischanged between the scan lines s₄ and s₅ as illustrated in the example2, the onboard device 1 regards a middle point between the scan lines s₄and s₅ as the end point of the white line. In a case where thereflection intensity of the scan data is changed between the scan liness₇ and s₈ as illustrated in the example 3, the onboard device 1 regardsa middle point between the scan lines s₇ and s₈ as the end point of thewhite line.

A method of detecting an end point of a termination part of the whiteline is basically similar. FIG. 10 illustrates a method of measuring aposition of the end point of the termination part of the white line(termination point of white line). It is assumed that the reflectionintensity of the scan data is changed between the scan lines s₂ and s₃as illustrated in the example 4. Namely, the reflection intensity of thescan line s₂ is smaller than the predetermined threshold, and thereflection intensity of the scan line s₃ is larger than or equal to thepredetermined threshold. In this case, the onboard device 1 regards themiddle point between the scan lines s₂ and s₃ as the end point of thewhite line.

Likewise, in a case where the reflection intensity of the scan data ischanged between the scan lines s₄ and s₅ as illustrated in the example5, the onboard device 1 regards the middle point between the scan liness₄ and s₅ as the end point of the white line. Further, in a case wherethe reflection intensity of the scan data is changed between the scanlines s₇ and s₈ as illustrated in the example 6, the onboard device 1regards the middle point between the scan lines s₇ and s₈ as the endpoint of the white line.

In a case where the reflection intensity of the scan data is changedbetween the i-th scan line s_(i) and the (i+1)-th scan line s_(i+1), ameasurement value L_(x)(t) of the lidar that indicates the position ofthe end point of the white line in the traveling direction of thevehicle is calculated by the following expression with use of a positionr_(x)(i) of the i-th scan line s_(i) and a position r_(x)(i+1) of the(i+1)-th scan line s_(i+1) in the traveling direction of the vehicle.

$\begin{matrix}{{L_{x}(t)} = \frac{{r_{x}(i)} + {r_{x}\left( {i + 1} \right)}}{2}} & (7)\end{matrix}$

The measurement value of the white line by each of the lidars 2 is givenby the following expression.

$\begin{matrix}{{Z(t)} = \begin{bmatrix}{L_{x}(t)} \\{L_{y}(t)}\end{bmatrix}} & (8)\end{matrix}$

Note that, the examples of FIG. 9 and FIG. 10 illustrate the case whereone scan line can be detected as a set of scan data having equivalentreflection intensity. However, there is a case where the reflectionintensity of the scan data is changed in a middle of one scan line. Inthis case, it is difficult to distinguish whether the scan linecorresponds to the end point position of the white line or the scan linedoes not correspond to the end point position of the white line but thescan line has been detected as the end point due to stains, blur, or thelike.

FIG. 11 illustrates an example in which the reflection intensity of thescan data is changed in the middle of the scan line. In the example 7and the example 8, the reflection intensity of the scan data is changedin the middle of the scan line s₃. Therefore, it is difficult todetermine that the scan line s₃ is the end point of the white line.Accordingly, in the case where the reflection intensity of the scan datais changed in the middle of the scan line, the onboard device 1 regardsa middle point between the scan line correctly detected as the whiteline and a scan line not detected as the white line at all, as the endpoint position of the white line. In the example 7 in FIG. 11, theonboard device 1 regards a middle point between the scan line s₂correctly detected as the white line and the scan line s₄ not detectedas the white line at all, as the end point position of the white line.Likewise, in the example 8, the onboard device 1 regards a middle pointbetween the scan line s₄ correctly detected as the white line and thescan line s₂ not detected as the white line at all, as the end pointposition of the white line.

In a case where the reflection intensity of the scan data is changed inthe middle of the i-th scan line s_(i) that is located between an(i−1)-th scan line s_(i−1) and the (i+1)-th scan line s_(i+1), themeasurement value L_(x)(t) of the lidar that indicates the position ofthe end point of the white line in the traveling direction of thevehicle is calculated by the following expression (9) with use of aposition r_(x)(i−1) of the (i−1)-th scan line s_(i−1) and the positionr_(x)(i+1) of the (i+1)-th scan line s_(i+1) in the traveling directionof the vehicle. In addition, the line interval d(t) at that time iscalculated by an expression (10).

$\begin{matrix}{\mspace{79mu} {{L_{x}(t)} = \frac{{r_{x}\left( {i - 1} \right)} + {r_{x}\left( {i + 1} \right)}}{2}}} & (9) \\{{d(t)} = {{{r\left( {i - 1} \right)}\cos \mspace{11mu} {\alpha \left( {i - 1} \right)}\cos \mspace{11mu} {\beta \left( {i - 1} \right)}} - {{r\left( {i + 1} \right)}\cos \mspace{11mu} {\alpha \left( {i + 1} \right)}\cos \mspace{11mu} {\beta \left( {i + 1} \right)}}}} & (10)\end{matrix}$

As described above, in the case where the end point of the white pointcan be detected, the measurement value L_(x)(t) in the travelingdirection of the vehicle has a value derived from the expression (7) orthe expression (9). In a case where the end point of the white linecannot be detected, the measurement value L_(x)(t) in the travelingdirection of the vehicle is absent. On the other hand, a measurementvalue L_(y)(t) in the lateral direction of the vehicle is an averagevalue of center points of the plurality of scan lines detected in thewindow W. As a result, the measurement value of the white line by thelidar can be determined based on the detection result of the white line.

(2) Own-Vehicle Position Estimation Method

Next, an own-vehicle position estimation method according to the presentembodiment is described.

(Calculation of Landmark Prediction Value)

First, a method of calculating a landmark prediction value of the endpoint of the white line by using the map data is described. FIG. 12illustrates the method of calculating the landmark prediction value ofthe end point of the white line. In FIG. 12, a vehicle V exists in aworld coordinate system, and a vehicle coordinate system is defined witha center of the vehicle V as an origin. Coordinates (m_(x)(k), m_(y)(k))of a white line end point k are stored in the map DB 10. The coordinatesof the white line end point k stored in the map DB 10 are coordinates inthe world coordinate system, and the onboard device 1 converts thecoordinates to coordinates in the vehicle coordinate system.Specifically, when coordinates of an estimated own-vehicle position ofthe vehicle V are x (t) and y (t), and an estimated own-vehicle azimuthis Ψ (t), the landmark prediction value of the white line end point k isexpressed by the following expression with use of a rotation matrix thatconverts the coordinate in the world coordinate system to thecoordinates in the vehicle coordinate system.

$\begin{matrix}{{\overset{\_}{Z}(t)} = {\begin{bmatrix}{{\overset{\_}{L}}_{x}(t)} \\{{\overset{\_}{L}}_{y}(t)}\end{bmatrix} = {{\begin{bmatrix}{\cos \mspace{11mu} {\overset{\_}{\psi}(t)}} & {\sin \mspace{11mu} {\overset{\_}{\psi}(t)}} \\{{- \sin}\mspace{11mu} {\overset{\_}{\psi}(t)}} & {\cos \mspace{11mu} {\overset{\_}{\psi}(t)}}\end{bmatrix}\begin{bmatrix}{{m_{x}(k)} - {\overset{\_}{x}(t)}} \\{{m_{y}(k)} - {\overset{\_}{y}(t)}}\end{bmatrix}} = {\quad\begin{bmatrix}{{\left\{ {{m_{x}(k)} - {\overset{\_}{x}(t)}} \right\} \cos \mspace{11mu} {\overset{\_}{\psi}(t)}} + {\left\{ {{m_{y}(k)} - {\overset{\_}{y}(t)}} \right\} \sin \mspace{11mu} {\overset{\_}{\psi}(t)}}} \\{{{- \left\{ {{m_{x}(k)} - {\overset{\_}{x}(t)}} \right\}}\sin \mspace{11mu} {\overset{\_}{\psi}(t)}} + {\left\{ {{m_{y}(k)} - {\overset{\_}{y}(t)}} \right\} \cos \mspace{11mu} {\overset{\_}{\psi}(t)}}}\end{bmatrix}}}}} & (11)\end{matrix}$

(Correction of Kalman Gain)

Next, a method of correcting the Kalman gain based on a detection stateof the end point of the white line is described. FIG. 13A illustratescorrection coefficients of the Kalman gain in the case where the onboarddevice 1 detects the end point of the white line. As the landmarkmeasurement value used in the own-vehicle position estimation, there area measurement value in the traveling direction of the vehicle and ameasurement value in the lateral direction of the vehicle. Therefore,the correction coefficient in the traveling direction of the vehicle isdefined as a traveling-direction correction coefficient a(t), and acorrection coefficient in the lateral direction of the vehicle isdefined as a lateral-direction coefficient b(t).

In a case where the onboard device 1 detects the end point of the whiteline in the window W, the onboard device 1 uses the position of the endpoint of the white line as the measurement value in the travelingdirection. However, as described above, since the measurement value ofthe end point position of the white line is determined as the middlepoint between the two adjacent scan lines before and after the change inthe reflection intensity, the measurement accuracy in the travelingdirection of the vehicle is varied depending on the line interval of thescan lines. Namely, the error is large when the line interval is wide,and the error is small when the line interval is narrow. Accordingly,the Kalman gain in the traveling direction is corrected using the lengthof the line interval of the scan lines. Specifically, an inverse numberof the line interval d(t) is defined as the traveling-directioncoefficient a(t). Namely, the traveling-direction coefficient a(t) isset as follows,

a(t)=1/d(t).

In the case where the end point of the white line is detected,measurement accuracy of the end point position of the white line ishigher as the line interval is narrower. Therefore, correction toincrease the Kalman gain is performed.

As the measurement value in the lateral direction of the vehicle, theaverage value of the center points of the plurality of scan linesdetected in the window W is used. In the case where the end point of thewhite line is detected in the window W, when the number of scan linesexisting in the window W is defined as N_(M), and the number of scanlines measured on the white line by the lidar is defined as N_(L) asillustrated in FIG. 13A, the lateral-direction coefficient b(t) is setas follows,

b(t)=N _(L)(t)/N _(M).

In the case where the end point of the white line is detected,measurement accuracy in the lateral direction of the vehicle is higheras the number of scan lines detected on the white line is larger.Therefore, correction to increase the Kalman gain is performed.

FIG. 13B illustrates the correction coefficients of the Kalman gain in acase where the onboard device 1 detects the white line but the onboarddevice 1 does not detect the end point of the white line. In the casewhere the onboard device 1 does not detect the white line, themeasurement value in the traveling direction of the vehicle cannot beobtained. Therefore, the traveling-direction coefficient a(t) is set asfollows,

a(t)=0.

Further, in the case where the onboard device 1 does not detect the endpoint of the white line, the center points of all of the scan lines inthe window W can be measured and the measurement accuracy in the lateraldirection of the vehicle is high. Therefore, the lateral-directioncoefficient b(t) is set as follows,

b(t)=1.

The own-vehicle position estimation unit 17 uses the traveling-directioncoefficient a(t) and the lateral-direction coefficient b(t) obtained inthe above-described manner to correct the Kalman gain. Specifically, theown-vehicle position estimation unit 17 multiplies the Kalman gain K(t)expressed in the following expression (12) by the traveling-directioncoefficient a(t) and the lateral-direction coefficient b(t), thereby togenerate an adaptive Kalman gain K(t)′ expressed in an expression (13).

$\begin{matrix}{{K(t)} = {{\overset{¯}{P}(t)}{H(t)}^{T}\left\{ {{{H(t)}{\overset{¯}{P}(t)}{H(t)}^{T}} + {R(t)}} \right\}^{- 1}}} & (12) \\{{K(t)}^{\prime} = {\left\lbrack {{a(t)} \times {k_{1}(t)}\mspace{14mu} {b(t)} \times {k_{2}(t)}} \right\rbrack = \begin{bmatrix}{{a(t)}{k_{11}(t)}\mspace{14mu} {b(t)}{k_{12}(t)}} \\{{a(t)}{k_{21}(t)}\mspace{14mu} {b(t)}{k_{22}(t)}} \\{{a(t)}{k_{31}(t)}\mspace{14mu} {b(t)}{k_{32}(t)}}\end{bmatrix}}} & (13)\end{matrix}$

Thereafter, the own-vehicle position estimation unit 17 applies theobtained adaptive Kalman gain K(t)′ to the Kalman gain K(t) in theexpression (2), and calculates an estimated own-vehicle positionX{circumflex over ( )}(t) by the following expression (14).

{circumflex over (X)}(t)= X (t)+K(t)′{Z(t)− Z (t)}  (14)

(Correction of Measurement Noise)

Next, a method of correcting measurement noise based on the detectionstate of the end point of the white line is described. FIG. 14Aillustrates correction coefficients of the measurement noise in the casewhere the onboard device 1 detects the end point of the white line. Asthe landmark measurement value used in the own-vehicle positionestimation, there are the measurement value in the traveling directionof the vehicle and the measurement value in the lateral direction of thevehicle. Therefore, the correction coefficient in the travelingdirection of the vehicle is defined as the traveling-directioncorrection coefficient a(t), and the correction coefficient in thelateral direction of the vehicle is defined as the lateral-directioncoefficient b(t).

In the case where the onboard device 1 detects the end point of thewhite line in the window W, the onboard device 1 uses the position ofthe end point of the white line as the measurement value in thetraveling direction. However, as described above, since the measurementvalue of the end point position of the white line is determined as themiddle point between the two adjacent scan lines before and after thechange in the reflection intensity, the measurement accuracy in thetraveling direction of the vehicle is varied depending on the lineinterval of the scan lines. Namely, the error is large when the lineinterval is wide, and the error is small when the line interval isnarrow. Accordingly, the measurement noise in the traveling direction iscorrected using the length of the line interval of the scan lines.Specifically, a ratio of the line interval d(t) and the shortest lineinterval (line interval d₉ in FIG. 9) among the plurality of detectedline intervals is defined as the traveling-direction coefficient a(t).Namely, the traveling-direction coefficient a(t) is set as follows,

a(t)=d(t)/d ₉.

In the case where the end point of the white line is detected, themeasurement accuracy of the end point position of the white line ishigher as the line interval is narrower. Therefore, correction to reducethe measurement noise is performed.

As the measurement value in the lateral direction of the vehicle, theaverage value of the center points of the plurality of scan linesdetected in the window W is used. In the case where the end point of thewhite line is detected in the window W, when the number of scan linesexisting in the window W is defined as N_(M), and the number of scanlines measured on the white line by the lidar is defined as N_(L) asillustrated in FIG. 14A, the lateral-direction coefficient b(t) is setas follows,

b(t)=N _(M) /N _(L)(t).

In the case where the end point of the white line is detected,measurement accuracy in the lateral direction of the vehicle is higheras the number of scan lines detected on the white line is larger.Therefore, correction to reduce the measurement noise is performed.

FIG. 14B illustrates the correction coefficients of the measurementnoise in the case where the onboard device 1 detects the white line butthe onboard device 1 does not detect the end point of the white line. Inthe case where the onboard device 1 does not detect the white line, themeasurement value in the traveling direction of the vehicle cannot beobtained. Therefore, a large value is set as the traveling-directioncoefficient a(t). For example, the traveling-direction coefficient a(t)is set as follows,

a(t)=10000.

Further, in the case where the onboard device 1 does not detect the endpoint of the white line, the center points of all of the scan lines inthe window W can be measured and the measurement accuracy in the lateraldirection of the vehicle is high. Therefore, the lateral-directioncoefficient b(t) is set as follows,

b(t)=1.

The own-vehicle position estimation unit 17 multiplies the measurementnoise by the traveling-direction coefficient a(t) and thelateral-direction coefficient b(t) obtained in the above-describedmanner, thereby to generate adaptive measurement noise. Further, theown-vehicle position estimation unit 17 uses the adaptive measurementnoise to determine the estimated own-vehicle position.

Specifically, basic measurement noise R(t) is derived from the followingexpression. Note that, with respect to the white line measurement as atarget, σ_(Lx) ² is measurement noise in the traveling direction in thevehicle coordinate system, and σ_(Ly) ² is measurement noise in thelateral direction in the vehicle coordinate system.

$\begin{matrix}{{R(t)} = \begin{bmatrix}\sigma_{Lx}^{2} & 0 \\0 & \sigma_{Ly}^{2}\end{bmatrix}} & (15)\end{matrix}$

Accordingly, the own-vehicle position estimation unit 17 multiplies themeasurement noise by the traveling-direction coefficient a(t) and thelateral-direction coefficient b(t) to obtain the following adaptivemeasurement noise R(t)′.

$\begin{matrix}{{R(t)}^{\prime} = \begin{bmatrix}{{a(t)}\sigma_{Lx}^{2}} & 0 \\0 & {{b(t)}\mspace{11mu} \sigma_{Ly}^{2}}\end{bmatrix}} & (16)\end{matrix}$

Further, the own-vehicle position estimation unit 17 substitutes theadaptive measurement noise R(t)′ into the expression (12) for the Kalmangain, to obtain the following adaptive Kalman gain K(t)′.

K(t)′= P (t)H(t)^(T) {H(t) P (t)H(t)^(T) +R(t)′}⁻¹   (17)

Thereafter, the own-vehicle position estimation unit 17 applies theobtained adaptive Kalman gain K(t)′ to the Kalman gain K(t) in theexpression (2), and calculates the estimated own-vehicle positionX{circumflex over ( )}(t) from the expression (14).

As a result, the position estimation in the traveling direction can beperformed using the end point information on the dashed line, and thesuitable processing based on the detection state of the white line canbe performed by the processing using the adaptive measurement noisecorresponding to the detection accuracy of the end point of the whiteline and the number of the detected lines.

(3) Own-Vehicle Position Estimation Processing

FIG. 15 is a flowchart of the own-vehicle position estimation processingperformed by the own-vehicle position estimation unit 17. Theown-vehicle position estimation unit 17 repeatedly performs theprocessing in the flowchart of FIG. 15.

First, the own-vehicle position estimation unit 17 determines whether avehicle body speed and an angular velocity in a yaw direction of thevehicle have been detected (step S11). For example, the own-vehicleposition estimation unit 17 detects the vehicle body speed based on theoutput of the vehicle speed sensor 4, and detects the angular velocityin the yaw direction based on the output of the gyro sensor 3. In a casewhere the vehicle body speed and the angular velocity in the yawdirection of the vehicle have been detected (step S11: Yes), theown-vehicle position estimation unit 17 uses the detected vehicle bodyspeed and the detected angular velocity to calculate the predictedown-vehicle position X (t) from the estimated own-vehicle positionX{circumflex over ( )}(t−1) of the previous time. Further, theown-vehicle position estimation unit 17 calculates the covariance matrixat the current time from the covariance matrix of the previous time(step S12). Note that, in the case where the vehicle body speed and theangular velocity in the yaw direction of the vehicle have not beendetected (step S11: No), the own-vehicle position estimation unit 17uses the estimated own-vehicle position X{circumflex over ( )}(t−1) ofthe previous time as the predicted own-vehicle position X−(t), and usesthe covariance matrix of the previous time as the covariance matrix atthe current time.

Next, the own-vehicle position estimation unit 17 determines whether thewhite lines have been detected as the landmark used in the own-vehicleposition estimation (step S13). Specifically, the own-vehicle positionestimation unit 17 sets the windows W as illustrated in FIG. 7, anddetermines whether the white lines have been detected in the windows Wbased on the reflection intensity of the scan data by the lidars 2. In acase where the white lines have not been detected (step S13: No), theown-vehicle position estimation using the white lines cannot beperformed. Therefore, the processing ends.

In contrast, in a case where the white lines have been detected (stepS13: Yes), the own-vehicle position estimation unit calculates thelandmark prediction values of the white lines based on the map data inthe map DB 10, and calculates the landmark measurement values of thewhite lines by the lidars 2 (step S14). Specifically, the own-vehicleposition estimation unit 17 converts the coordinates of the end pointsof the white lines included in the map data into coordinates in thevehicle coordinate system, and calculates the landmark prediction valueof the end points of the white lines. In addition, the own-vehicleposition estimation unit 17 uses the measurement values of the whitelines by the lidars as the landmark measurement values.

Further, as described with reference to FIG. 13 and FIG. 14, theown-vehicle position estimation unit 17 calculates thetraveling-direction coefficient a(t) and the lateral-directioncoefficient b(t) based on whether the end points of the white lines havebeen detected (step S15).

Thereafter, the own-vehicle position estimation unit 17 uses thecovariance matrix to calculate the Kalman gain K(t) based on theabove-described expression (10), uses the traveling-directioncoefficient a(t) and the lateral-direction coefficient b(t) to generatethe adaptive Kalman gain K(t)′ expressed in the expression (13) or (17),and calculates the estimated own-vehicle position from the expression(14). Moreover, the own-vehicle position estimation unit 17 uses theadaptive Kalman gain K(t)′ to update the covariance matrix as expressedby the following expression (18) (step S16).

P(t)= P (t)−K(t)′H(t) P (t)   (18)

As described above, in the case where the end points of the white linescan be detected, the own-vehicle position estimation unit 17 performsthe own-vehicle position estimation processing based on the Kalmanfilter by using the end points of the white lines as the landmarks.Further, the own-vehicle position estimation unit 17 calculates thetraveling-direction coefficient and corrects the Kalman gain based onthe line interval of the scan lines by each of the lidars 2 when the endpoints of the white lines are detected. This makes it possible toperform the own-vehicle position estimation with high accuracy in thetraveling direction of the vehicle.

[Modification]

In the above-described embodiment, the windows W are provided on theright and left positions in front of the vehicle, and the onboard device1 detects the white lines in the windows W. In a case where a pair oflidars 2 is provided on a rear part of the vehicle, the windows W may beprovided on the right and left positions on a rear side of the vehicleto detect the white lines.

BRIEF DESCRIPTION OF REFERENCE NUMBERS

-   1 Onboard device-   2 Lidar-   3 Gyro sensor-   4 Vehicle speed sensor-   5 GPS receiver-   10 Map DB

1. A self-position estimation device mounted on a mobile bodycomprising: an acquisition unit configured to acquire a predictedposition of the mobile body; a difference value calculation unitconfigured to calculate a difference value between a predicted positionof an end point of a lane division line and a measured position of theend point of the lane division line, the predicted position beingobtained based on information on the end point of the lane division lineacquired from map information, the measured position being measured by ameasurement unit which is mounted on the mobile body and which measuresthe measured position by performing scanning with a light in apredetermined direction; and an estimation unit configured to estimate aself-position of the mobile body by correcting the predicted position ofthe mobile body with a value obtained by multiplying the differencevalue by a coefficient, wherein the estimation unit corrects thecoefficient based on an interval of scanning positions of themeasurement unit at a position where the end point of the lane divisionline is detected.
 2. The self-position estimation device according toclaim 1, wherein the estimation unit corrects the coefficient with aninverse number of the interval of the scanning positions.
 3. Theself-position estimation device according to claim 1, wherein theestimation unit corrects the coefficient based on a ratio of theinterval of the scanning positions and a shortest interval of thescanning positions measured by the measurement unit among intervals ofthe scanning positions.
 4. The self-position estimation device accordingto claim 1, wherein the measurement unit detects a lane division lineexisting in a window that is defined at a predetermined position with aposition of the mobile body as a reference, and wherein the estimationunit corrects the coefficient based on a ratio of number of scan linesexisting on the lane division line and number of scan lines previouslydetermined based on a size of the window.
 5. The self-positionestimation device according to claim 1, wherein the measurement unitdetects a middle point between a scan line existing on the lane divisionline and a scan line not existing on the lane division line as the endpoint of the lane division line, the scan line existing on the lanedivision line and the scan line not existing on the lane division linebeing adjacent to each other.
 6. The self-position estimation deviceaccording to claim 1, wherein the coefficient is a Kalman gain.
 7. Aself-position estimation method performed by a self-position estimationdevice mounted on a mobile body, the method comprising: an acquisitionprocess configured to acquire a predicted position of the mobile body; adifference value calculation process configured to calculate adifference value between a predicted position of an end point of a lanedivision line and a measured position of the end point of the lanedivision line, the predicted position being obtained based oninformation on the end point of the lane division line acquired from mapinformation, the measured position being measured by a measurement unitwhich is mounted on the mobile body and which measures the measuredposition by performing scanning with a light in a predetermineddirection; and an estimation process configured to estimate aself-position of the mobile body by correcting the predicted position ofthe mobile body with a value obtained by multiplying the differencevalue by a coefficient, wherein the estimation process corrects thecoefficient based on an interval of scanning positions of themeasurement unit at a position where the end point of the lane divisionline is detected.
 8. A non-transitory computer-readable medium storing aprogram executed by a self-position estimation device that is mounted ona mobile body and includes a computer, the program causing the computerto function as: an acquisition unit configured to acquire a predictedposition of the mobile body; a difference value calculation unitconfigured to calculate a difference value between a predicted positionof an end point of a lane division line and a measured position of theend point of the lane division line, the predicted position beingobtained based on information on the end point of the lane division lineacquired from map information, the measured position being measured by ameasurement unit which is mounted on the mobile body and which measuresthe measured position by performing scanning with a light in apredetermined direction; and an estimation unit configured to estimate aself-position of the mobile body by correcting the predicted position ofthe mobile body with a value obtained by multiplying the differencevalue by a coefficient, wherein the estimation unit corrects thecoefficient based on an interval of scanning positions of themeasurement unit at a position where the end point of the lane divisionline is detected.
 9. (canceled)